
import string

import numpy



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				return b if a == 0 else greatest_common_divisor(b % a			,    A_  )






class        __lowercase		:
					__magic_name__    :				str  						=						string.ascii_uppercase + string.digits
					# This cipher takes alphanumerics into account
					# i.e. a total of 36 characters

					# take x and return x % len(key_string)
					__magic_name__    :				Tuple  						=						numpy.vectorize(lambda A							: x % 36							)

					__magic_name__    :				Optional[Any]  						=						numpy.vectorize(_A							)


					def __init__(						self							,							a__    )		->				None:





									'''simple docstring'''
									A_													=	self.modulus(UpperCamelCase__    )  # mod36 calc's on the encrypt key
									self.check_determinant()  # validate the determinant of the encryption key
									A_													=	encrypt_key.shape[0]


					def 							lowerCAmelCase_     (						self							,							a__    )		->				int:





									'''simple docstring'''
									return self.key_string.index(UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				str:





									'''simple docstring'''
									return self.key_string[round(UpperCamelCase__    )]


					def 							lowerCAmelCase_     (						self    )		->				None:





									'''simple docstring'''
									A_													=	round(numpy.linalg.det(self.encrypt_key    )    )

									if det < 0:
													A_													=	det % len(self.key_string    )

									A_													=	len(self.key_string    )
									if greatest_common_divisor(UpperCamelCase__							,							len(self.key_string    )    ) != 1:
													A_													=	(
													    F"determinant modular {req_l} of encryption key({det}) "
													    F"is not co prime w.r.t {req_l}.\nTry another key."
													)
													raise ValueError(UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				str:





									'''simple docstring'''
									A_													=	[char for char in text.upper() if char in self.key_string]

									A_													=	chars[-1]
									while len(UpperCamelCase__    ) % self.break_key != 0:
													chars.append(UpperCamelCase__    )

									return "".join(UpperCamelCase__    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				str:





									'''simple docstring'''
									A_													=	self.process_text(text.upper()    )
									A_													=	''''''

									for i in range(0							,							len(UpperCamelCase__    ) - self.break_key + 1							,							self.break_key    ):
													A_													=	text[i : i + self.break_key]
													A_													=	[self.replace_letters(UpperCamelCase__    ) for char in batch]
													A_													=	numpy.array([vec]    ).T
													A_													=	self.modulus(self.encrypt_key.dot(UpperCamelCase__    )    ).T.tolist()[
													    0
													]
													A_													=	''''''.join(
													    self.replace_digits(UpperCamelCase__    ) for num in batch_encrypted    )
													encrypted += encrypted_batch

									return encrypted


					def 							lowerCAmelCase_     (						self    )		->				numpy.ndarray:





									'''simple docstring'''
									A_													=	round(numpy.linalg.det(self.encrypt_key    )    )

									if det < 0:
													A_													=	det % len(self.key_string    )
									A_													=	None
									for i in range(len(self.key_string    )    ):
													if (det * i) % len(self.key_string    ) == 1:
																	A_													=	i
																	break

									A_													=	(
									    det_inv
									    * numpy.linalg.det(self.encrypt_key    )
									    * numpy.linalg.inv(self.encrypt_key    )
									)

									return self.to_int(self.modulus(UpperCamelCase__    )    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				str:





									'''simple docstring'''
									A_													=	self.make_decrypt_key()
									A_													=	self.process_text(text.upper()    )
									A_													=	''''''

									for i in range(0							,							len(UpperCamelCase__    ) - self.break_key + 1							,							self.break_key    ):
													A_													=	text[i : i + self.break_key]
													A_													=	[self.replace_letters(UpperCamelCase__    ) for char in batch]
													A_													=	numpy.array([vec]    ).T
													A_													=	self.modulus(decrypt_key.dot(UpperCamelCase__    )    ).T.tolist()[0]
													A_													=	''''''.join(
													    self.replace_digits(UpperCamelCase__    ) for num in batch_decrypted    )
													decrypted += decrypted_batch

									return decrypted



def 							lowerCamelCase_	( ):
				A_													=	int(input('''Enter the order of the encryption key: '''  )  )
				A_													=	[]

				print('''Enter each row of the encryption key with space separated integers'''  )
				for _ in range(A_  ):
								A_													=	[int(A_  ) for x in input().split()]
								hill_matrix.append(A_  )

				A_													=	HillCipher(numpy.array(A_  )  )

				print('''Would you like to encrypt or decrypt some text? (1 or 2)'''  )
				A_													=	input('''\n1. Encrypt\n2. Decrypt\n'''  )
				if option == "1":
								A_													=	input('''What text would you like to encrypt?: '''  )
								print('''Your encrypted text is:'''  )
								print(hc.encrypt(A_  )  )
				elif option == "2":
								A_													=	input('''What text would you like to decrypt?: '''  )
								print('''Your decrypted text is:'''  )
								print(hc.decrypt(A_  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						main()

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

